This research application aims to investigate how neuromotor constraints arising from normal aging and from impairment result in different dynamic organization of functional units during speech. Studies examine how multiple articulators (lips, jaw, and voice) are coordinated to achieve perceptually accurate speech under normal and mechanically perturbed speaking conditions. Individuals with the speech motor programming disorder of Apraxia of Speech (AOS) will be considered. AOS disrupts inter-articulator coordination. Research has suggested that specific muscle groups or articulators are dynamically organized, as functional units, according to neuromotor and mechanical constraints of the speech system. As a result, it is predicted that the organization of these units will vary as a function of the neuromotor abilities of the speaker and the demands of the speaking task. Under the assumption that the neuromotor constraints arising from the normal aging process and those of AOS are different, it is predicted that different patterns of motor organization will be found in speakers with AOS versus young and older typical speakers in response to mechanical perturbation. These differences in organization will be tested by examining timing of voice offset and onset and kinematic properties (relative displacement and duration for lip closure, peak velocity and acceleration of lip closing and opening) of the lips and jaw in the production of/papa/under typical and perturbed conditions. In healthy speakers, two speaking rates will be tested to determine whether speech rate can explain any effects of normal aging or impairment. Perturbation will consist of an unexpected 50 g load applied downward on the lower lip either early (approximately 70 msec prior to onset of lip closure) or late (approximately 20 msec after onset of closure). It has been suggested that response to early perturbations reflects motor programming processes while response to late perturbations reflects movement execution processes. AOS is an ideal system as it only disrupts the former. The DIVA neural network model is used to develop specific predictions and results will be interpreted in the context of this and other current models of motor control.